WO2017120176A1 - Dispositif et système pour mesures individuelles d'exposition aux uv - Google Patents

Dispositif et système pour mesures individuelles d'exposition aux uv Download PDF

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Publication number
WO2017120176A1
WO2017120176A1 PCT/US2017/012108 US2017012108W WO2017120176A1 WO 2017120176 A1 WO2017120176 A1 WO 2017120176A1 US 2017012108 W US2017012108 W US 2017012108W WO 2017120176 A1 WO2017120176 A1 WO 2017120176A1
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Prior art keywords
exposure
radiation
different
personal
measured
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PCT/US2017/012108
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English (en)
Inventor
Yunzhou Shi
Rafal Pielak
Guive BALOOCH
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L'oreal
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Priority to JP2018534934A priority Critical patent/JP6735832B2/ja
Priority to CN201780005668.7A priority patent/CN108472499B/zh
Priority to KR1020187022529A priority patent/KR102096018B1/ko
Priority to EP17736221.7A priority patent/EP3400062A4/fr
Publication of WO2017120176A1 publication Critical patent/WO2017120176A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/06—Radiation therapy using light
    • A61N5/0613—Apparatus adapted for a specific treatment
    • A61N5/0616—Skin treatment other than tanning
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/02—Details
    • G01J1/0233—Handheld
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/02—Details
    • G01J1/0219—Electrical interface; User interface
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/02—Details
    • G01J1/0238—Details making use of sensor-related data, e.g. for identification of sensor or optical parts
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/02—Details
    • G01J1/0271—Housings; Attachments or accessories for photometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/02—Details
    • G01J1/029—Multi-channel photometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/42—Photometry, e.g. photographic exposure meter using electric radiation detectors
    • G01J1/4228—Photometry, e.g. photographic exposure meter using electric radiation detectors arrangements with two or more detectors, e.g. for sensitivity compensation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/42—Photometry, e.g. photographic exposure meter using electric radiation detectors
    • G01J1/429—Photometry, e.g. photographic exposure meter using electric radiation detectors applied to measurement of ultraviolet light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/48—Photometry, e.g. photographic exposure meter using chemical effects
    • G01J1/50—Photometry, e.g. photographic exposure meter using chemical effects using change in colour of an indicator, e.g. actinometer
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/06—Radiation therapy using light
    • A61N2005/0626—Monitoring, verifying, controlling systems and methods
    • A61N2005/0627—Dose monitoring systems and methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/06—Radiation therapy using light
    • A61N2005/0626—Monitoring, verifying, controlling systems and methods
    • A61N2005/0627—Dose monitoring systems and methods
    • A61N2005/0628—Dose monitoring systems and methods including a radiation sensor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/06—Radiation therapy using light
    • A61N2005/0658—Radiation therapy using light characterised by the wavelength of light used
    • A61N2005/0661—Radiation therapy using light characterised by the wavelength of light used ultraviolet
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/02—Details
    • G01J1/0266—Field-of-view determination; Aiming or pointing of a photometer; Adjusting alignment; Encoding angular position; Size of the measurement area; Position tracking; Photodetection involving different fields of view for a single detector
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/02—Details
    • G01J2001/0257—Details portable
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/02—Details
    • G01J2001/0276—Protection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/42—Photometry, e.g. photographic exposure meter using electric radiation detectors
    • G01J2001/4266—Photometry, e.g. photographic exposure meter using electric radiation detectors for measuring solar light
    • G01J2001/428—Photometry, e.g. photographic exposure meter using electric radiation detectors for measuring solar light for sunlight scattered by atmosphere
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/46—Measurement of colour; Colour measuring devices, e.g. colorimeters
    • G01J2003/466—Coded colour; Recognition of predetermined colour; Determining proximity to predetermined colour

Definitions

  • the present disclosure relates to a system and method for determining an amount of UV exposure for a particular user based on a detection of the UV exposure at a location of the user and specific information regarding the particular user.
  • UV radiation Excessive ultraviolet (UV) radiation has acute and chronic effects on the skin, eye and immune system. Personalized monitoring of UV radiation is thus paramount to measure the extent of personal sun exposure, which could vary with environment, lifestyle, and sunscreen use.
  • UV radiation is essential for production of vitamin D and beneficial for human health, but over-exposure to UV has many associated risk factors, including skin cancer and photo-aging, even long after UV exposure ends.
  • the acute effects of excessive UVA and UVB exposure are usually short-lived and reversible. Such effects include erythema, pigment darkening and sunburn. Prolonged exposures even to sub-erythemal UV doses result in epidermal thickening and degradation of keratinocytes, elastin, collagen and blood vessels, thus leading to premature skin aging. Clinical symptoms usually include increased wrinkling and loss of elasticity.
  • Studies have also shown that both UVA and UVB radiation have local and systemic immunosuppressive properties, which is believed to be an important contributor to skin cancer development.
  • UV- induced DNA damage is an important factor in developing all types of skin cancer including melanoma, non-melanoma skin cancers, basal cell carcinoma and squamous cell carcinoma.
  • Both UVA and UVB are strongly scattered by air, aerosols, and clouds. For high sun angles, when most of the UV arrives, cloud effects are similar at UVA and UVB wavelengths; however, for low sun conditions, the UVB attenuation tends to be stronger.
  • UVA penetrates glass windows and therefore may result in excessive UV exposures even in an indoor environment.
  • UVA readily passes through the ozone layer resulting in higher intensities of the UVA portion of the solar spectrum at the earth surface. Continuous sunscreen protection and monitoring of personal UV exposures is therefore critical for better skin protection and prevention of skin cancer.
  • a device configured to measure ultra-violet (UV) radiation exposure, comprising: a surface that includes a plurality of different sections that each have a different sensitivity to UV radiation exposure, wherein each of the plurality of different sections are configured to display a different color in response to the UV radiation exposure.
  • UV radiation exposure comprising: a surface that includes a plurality of different sections that each have a different sensitivity to UV radiation exposure, wherein each of the plurality of different sections are configured to display a different color in response to the UV radiation exposure.
  • the plurality of different sections include a different UV responsive chemical deposited thereon.
  • the UV responsive chemical is a UV responsive ink.
  • the plurality of different sections include a different UV responsive electrical element.
  • a system for determining personal ultra-violet
  • UV radiation measurements comprising: a measurement device configured to measure UV irradiation; and a terminal device configured to receive or capture an output of the measured UV irradiation from the measurement device and to determine a specific user's personal UV exposure risk level based on at least the measured sun irradiation and information of a skin type of the specific user.
  • the measurement device includes a surface that includes a plurality of different sections that each have a different sensitivity to UV radiation exposure, wherein each of the plurality of different sections are configured to display a different color in response to the UV radiation exposure
  • the terminal device includes an image capturing device configured to capture an image of the plurality of different sections as the captured output of the measured UV radiation, and processing circuitry configured to determine the measured UV radiation based on performing image analysis of the captured image.
  • the terminal device is configured to receive information from an external device of a personal UV dose amount for the specific user based on at least the information of the skin type of the user and the measured UV irradiation.
  • the terminal device is configured to output a recommended method of protection based on the determined personal UV exposure risk level of the specific user.
  • a system for determining personal ultra-violet (UV) radiation measurements, comprising: a measurement device configured to measure UV irradiation; and a terminal device configured to receive or capture an output of the measured UV irradiation from the measurement device and to determine a specific user's personal UV exposure risk level based on at least the measured sun irradiation and information of a skin type of the specific user.
  • UV ultra-violet
  • the measurement device includes a surface that includes a plurality of different sections that each have a different sensitivity to UV radiation exposure, wherein each of the plurality of different sections are configured to display a different color in response to the UV radiation exposure
  • the terminal device includes an image capturing device configured to capture an image of the plurality of different sections as the captured output of the measured UV radiation, and processing circuitry configured to determine the measured UV radiation based on performing image analysis of the captured image.
  • the terminal device is configured to receive information from an external device of a personal UV dose amount for the specific user based on at least the information of the skin type of the user and the measured UV irradiation.
  • the plurality of different sections include a different UV responsive chemical deposited thereon.
  • the UV responsive chemical is a UV responsive ink.
  • the plurality of different sections include a different UV responsive electrical element.
  • the image analysis includes at least one of a) a shape recognition and features location algorithm; b) a lighting condition correction algorithm; c) a color quantification 5 algorithm; and d) a UV dose determination algorithm.
  • Fig. 1 illustrates a system for personal UV exposure measurements.
  • Fig. 2 provides additional details regarding the system.
  • Figs. 3A and 3B illustrate a series of processes performed in the system.
  • Fig. 4 shows outputs of a user interface after all analyses are performed.
  • Fig. 5 shows examples of reference values for the color of each square of the UV patch.
  • Fig. 6 shows examples of the color change of certain squares with an increase in UV exposure.
  • Fig. 7 shows UV patch examples corresponding to different UV values.
  • Fig. 8 shows a UV sensor structure.
  • A Construction of the UV sensor (from the top to 20 the bottom): protective liner with adhesive, permeable polyurethane (TPU, 16 ⁇ ) with printed UV ink, UV blockers and reference colors, top skin adhesive layer (25 ⁇ ), NFC antenna (yellow, 18 ⁇ ) and a polyimide film (PI, 12.7 ⁇ ), NFC tag (0.5 mm), polyethylene terephthalate layer (PET, 12 ⁇ ), bottom skin adhesive layer (25 ⁇ ), and bottom liner.
  • B The front of the UV sensor patch.
  • D Wearing 25 the UV sensor patch on the back of one's hand.
  • E Reading the UV sensor patch using the My UV Patch app.
  • Fig. 9 shows the mechanism of the UV sensor color change and color change quantification.
  • the UV sensor patch is composed of a series of reference colors 1 to 10, UV variable ink squares 1 1 to 16, and UV reversible ink squares 17 and 1 8.
  • the reference colors 1 - 30 10 correspond to the different colors of the UV ink squares when they are exposed to UV
  • the six UV sensitive ink squares change colors at distinctive rates when exposing to UVA radiation with square 1 1 being the most sensitive and square 16 the least sensitive. The color change is quantified in CIE Lab color space.
  • C Schematics showing the UV sensor patch before and after exposure to UVA radiation.
  • Fig. 10 shows he app algorithm flowchart.
  • Fig. 1 1 shows Table 1 (Personal UV daily sunstock) and Table 2 (Personal UV risk determination).
  • Fig. 12 shows a comparison of the UV patch and Scienterra UV dosimeter.
  • Fig. 13 shows a clinical evaluation of the UV patch.
  • A The study subjects wore the UV patches and Scienterra dosimeters during regular city and beach activities. Both devices showed agreement in UV dose measurements.
  • B The study subjects conducted controlled activity: single file walk in specified directions. The activity was repeated in the morning, afternoon, and evening. Each study subject wore one Scienterra dosimeter and two UV patches: one without sunscreen and the other one with sunscreen applied on it. Both the electronic dosimeter and the UV patch without sunscreen showed consistent results. The patch covered with sunscreen showed significant reduction in measured UV radiation.
  • Fig. 14 shows a comparison of UV readings among patch image analysis, Scienterra dosimeter and the mobile application.
  • UVA readings by patch picture analysis showed good correlation with Scienterra dosimeter readings, which validates the UV sensor image technique (B).
  • B the UV sensor image technique
  • C Scienterra dosimeter and app reading
  • D The 95% prediction ellipse is shown. The strong correlation among the three measurements further validates the sensor system.
  • Fig. 15 shows an example of world average UV exposure.
  • a world average UV exposure is generated based on the My UV Patch app user data from June 6 th , 2016 to August 18 th , 2016 (Fig.7A). Zoom in maps are shown for continental US (B) and part of Europe (C). The country and state that contributed the data are labeled in yellow to red, color map is generated by normalizing the UV exposures to range between 0 (minimum UV exposure, yellow) and 1 (maximum UV exposure, red).
  • Fig. 1 illustrates a system 100 for personal UV exposure measurements. It can be seen that the system includes one or more measurement devices 101 which may be at different geographical locations. Each of the devices 101 may connect to a user device 102, which may be a computer, tablet, personal digital assistant, or smartphone.
  • the device 101 may be referred to as a "patch" throughout this description, and it is configured to be attached to a skin surface of the user.
  • the user device is configured to receive an input from the user on the user's skin type in order to determine personal UV level.
  • the user device is also configured to receive inputs from external servers (via an internet connection, for example) such as UVI analytics server, a weather forecast server, and/or a pollution analytics server.
  • the user device is further configured to connect to a cloud computing environment which is connected to data analytics servers for determining personalized UV doses for the user based on information provided by the user device according to the above-noted inputs.
  • the data analytics servers may determine a personalized UV dose for the user based on one or more of a Patch ID, skin type, time series data cumulative UVA exposure, date/time/location information, and conversion of UVA to UVB and total UV.
  • the device 102 may determine a personalized UV dose for the user based on one or more of a Patch ID, skin type, time series data cumulative UVA exposure, date/time/location information, and conversion of UVA to UVB and total UV.
  • Fig. 2 provides additional details regarding the system 100.
  • Fig. 2 illustrates that the measurement device 101 may be composed of a series of UV responsive inks with different sensitivities and corresponding set of reference colors for data interpretation.
  • a non-limiting example of a UV responsive ink is CR234-BT2B by Spectra Group Limited.
  • the embodiments are not limited to using a chemical UV detection element, and the device 101 may use an electrical UV responsive element such as a UV diode or photodiode.
  • Fig. 2 further illustrates that the device 102 may be programmed with mobile software that provides a personalized UV exposure "coach" that provides one or more of image recognition, selection and processing; overall UV exposure; UV exposure risk level based on skin type and UV intensity; a time series UV exposure tracker; and a sunscreen advisor.
  • An operation of the system is as follows. Interrogated by the sun, the device 101 measures changes due to sun irradiation by means of chemical or electrical change. Mobile devices 102 are used to read such changes and convert to UV doses. Users use the mobile devices to communicate with cloud/server to upload personal information and download information for personal UV calculation. The cloud/server collects personalized information that includes date, time, location, skin type, and UV (UVA, UVB, UVI levels).
  • Figs. 3A-3B illustrate a series of processes performed in the system.
  • a patch/user ID is obtained by scanning a NFC tag on the device 101/patch with the smart phone 102.
  • the system allows for tracking multiple users with the same smart phone by scanning different patches.
  • images of a respective patch are obtained by the smartphone image capturing device (camera) function.
  • the smartphone is configured to perform quality control by analyzing for repetitiveness and rejecting unreliable images.
  • the smartphone is configured, for example, to extract 3 images within a certain tolerance and average.
  • the smartphone is further configured to perform image correction on the captured image of the patch by correcting distortion, reflection, uneven illumination, white balance, or a printing artifact.
  • the smartphone is further configured to perform image analysis.
  • image analysis can be used to provide quality control by locating the reference colors on the patch, analyzing for linearity for quality control, and correcting for nonlinearity. More specifically, the smartphone performs image analysis to locate the target color, compare the target color to reference colors, and obtain UV values using pre-determined UV/color calibration.
  • Phase II of Fig. 3B shows an algorithm of UVA/UVB conversion, which takes into account different factors such as Ozone climatology; solar zenith angle (SZA), elevation, and aerosol climatology, each of which may be based on one or more of latitude, longitude, and date/time of the device 101.
  • SZA solar zenith angle
  • elevation elevation
  • aerosol climatology each of which may be based on one or more of latitude, longitude, and date/time of the device 101.
  • Phase III of Fig.4 shows outputs of a user interface after all analyses are performed. For instance, an output may indicate whether or not the UV exposure for the user is considered safe, whether a sunscreen with a certain SPF is recommended, or if the UV levels are high or low on the given day.
  • UV device Part 1 Extract color change
  • the input is the picture taken by the cell phone camera.
  • the color of each square (See Fig. 5) is represented in CIE L*a*b space (denoted as L, A, B), where a values are used for quantification.
  • a value is a continuous number, format double-precision floating-point.
  • Square 1 to 10 are reference colors, a value is increasing from 1 to 10
  • Square 1 1 to 16 are UV variable colors (see also Fig. 6), where . Square 17 and 18 are reversible UV variable colors, will be compared to A 1 A 10 , and find a match, is equal or less than half of the deviation between two adjacent colors).
  • the output is the UV exposure, which is pre-determined by a look up table as follows:
  • the final output UV value will be the intersection
  • a value is a continuous number, format double-precision floating-point. In picl , they are 128, 129, 134, 136, 139, 141 , 145, 147, 151 , 153. a. If there are too many outliers, the picture is not evenly illuminated, ask for retaking the image. b. A 1 ⁇ A 2 ⁇ A 3 ⁇ A 4 ⁇ A 5 ⁇ A 6 ⁇ A 7 ⁇ A 8 ⁇ A 9 ⁇ A 10 , if not, the picture has over-exposure or under-exposure.
  • UV device Part 2 Algorithm to convert color change to UVA and UVB
  • a pre-determined look up table is extracted as follows:
  • A is equal or less than half of the deviation between two adjacent colors).
  • UV j linear interpolation between
  • pic 7-1 corresponds to UV 0.001
  • pic 7-2 corresponds to UV 0.01
  • pic 7-3 corresponds to UV 0.1
  • pic7-4 corresponds to UV 0.02
  • pic7-5 corresponds to UV 0.3
  • pic 7-6 corresponds to UV 0.005
  • pic 7-7 corresponds to UV 0.05
  • pic 7-8 corresponds to UV 0.5
  • pic 7-9 corresponds to UV 0.8
  • pic 7- 10 corresponds to UV 1.2 (should have a readout
  • Part A Core algorithm for conversion
  • UVA values are obtained from algorithm Part 1 listed above.
  • UVI the above table uses Ozone and SZA, additional factors include elevation, aerosols condition, and cloud condition. Additional factors may be included in later versions.
  • Ozone is calculated from latitude, longitude and date. Ozone can also be obtained using Ozone climatology through historical data (less accurate). 4. Calculate the Solar Zenith Angle for a given day, time, latitude and longitude. Source code information:
  • Ozone information may be obtained from a web service that downloads NOAA
  • relevant grib2 files convert the grib2 file into readable format, and parse the Ozone information for MSF to download.
  • the Ozone files downloaded by the app contains 3-day forecast (They are the 12 hours (noon), 60 hours (day 3) and 108 hours (day 5) by local time.
  • the app will use values from the last stored Ozone file (3 day forecast on day 1 , 3, 5).
  • the forecast is on day 1 , 3, 5.
  • Day 1 and 2 will use day l 's value
  • day 3 and 4 will use day 4's value
  • day 5 and 6 will use day 5's value.
  • the app will run calculation based on the closest time, but display an estimated value in the offline mode.
  • the first line gives the forecast date (UTC), and the filename includes the number of hours after UTC that it applies to, (e.g., 00 hours in this case).
  • the output is 10 values per line with 6516 lines, so that corresponds to a matrix of 360 x 181. ).
  • MEDs can be expressed in pure UVB or total UV (UVB+UVA) (mJ/cm 2 )
  • the lowest UV dose at risk for phototype I is 1835 mJ/cm 2 (about 100 mJ/cm 2 UVB and 1700 mJ/cm 2 UVA) that means that the patch should be sensitive to a dose of 2000 mJ/cm 2
  • UVA part • The different levels are based on the UVA dose threshold able to induce damage
  • UVA doses are expressed in mJ/cm 2
  • the dose at risk has been shown to be 15000 mJ/cm 2
  • safe dose could be defined as 7500 mJ/cm 2 and too high dose as 20000 mJ/cm 2
  • the above-described system and algorithm can adapted for other forms of input that may be provided by the measurement device 101 (e.g. other chemical, electrochemical, electrical, etc. )
  • UV Patch Sensor Additional Details While an overall system and algorithm were described above, below is a detailed description of the UV patch according to an embodiment.
  • the patch contains functional layers of ultrathin stretchable electronics and a photosensitive patterned dye that reacts to UV light. Color changes in the photosensitive dyes correspond to UV light intensity and are analyzed with a smartphone camera.
  • a software application on the smartphone has feature recognition, lighting condition correction, and quantification algorithms that detect and quantify changes in color. These changes in color are then correlated to corresponding shifts in UV dose, and compared to existing UV dose risk levels.
  • the soft mechanics of the UV patch allow for multiday wear in the presence of sunscreen and water. Two clinical studies serve to demonstrate the utility of the UV patch during daily activities with and without sunscreen application.
  • corresponding UVB exposure is calculated using a pre-computed lookup table that gives the conversion factor as a function of the column amount of ozone in the atmosphere and sloar zenith angle (SZA).
  • SZA sloar zenith angle
  • the first study demonstrated device functionality in different real life activities including swimming in the ocean, beach activities, showering, as well as compatibility with sunscreen and skin care product applications. It also helped us to further optimize and calibrate the device for accurate UV dose measurements.
  • the second study demonstrated patch UV readout accuracy during controlled and real life daily activities.
  • the UV patch is designed to conform to the skin surface, mimics skin mechanical properties and interaction with sunscreens. When the patch is attached to the skin, it experiences similar UV radiation as the surrounding skin. An exposure to UV radiation results in patch color change, which is quantified using a smartphone app (Fig. 8).
  • the UV sensing mechanism is composed of UV sensitive inks and blockers that are printed on a permeable polyurethane (TPU) film. Below the TPU, the patch contains a Near Field Communication (NFC) chip and copper/plyimide (PI) antenna for communication with a smartphone.
  • NFC Near Field Communication
  • PI copper/plyimide
  • a thin layer of polyethylene terephthalate (PET) prevents the NFC and antenna from directly contacting user's skin. Below the PET layer, there is a thin layer of skin adhesive that couple the UV patch with the skin (Fig. 8A).
  • the UV patch When exposed to UV radiation the patch changes color, which is quantified by image processing algorithms (Fig. 9).
  • the UV patch is composed of ten reference color squares 1 to 10 and six irreversible UV sensitive ink squares 1 1 to 16 (Fig. 9A).
  • the six UV variable ink squares were optimized to change color at progressively decreasing rates in order to cover broad sensitivity range. This also allows us to average readouts from multiple squares for better data accuracy (Fig. 9B).
  • the ten reference colors are blue with 10 to 100% transparency by steps of 10%, respectively, with a minimum ⁇ of 5 in between adjacent colors using the international commission on illumination's distance metric for colors.
  • a UV sensor patch from pre-exposure to fully exposed to UV is shown in Fig. 9C.
  • the UVA dosage is measured by quantifying the color change of the six UV variable ink squares.
  • the image of the UV sensor patch is captured and processed by a cell phone app.
  • the app algorithm is design to determine user's skin sensitivity to UV.
  • the app also determines user's location and the UV Index in the area.
  • the app can calculates users personal UV doses and risk level and recommends sunscreen product that provides the best protection and comfort.
  • the algorithm for the personal UV dose quantification include 4 subalgorithms: a) shape recognition and features location algorithm; b) lighting condition correction algorithm; c) color quantification algorithm; d) UV dose determination algorithm (Fig. 10).
  • the shape recognition algorithm is designed to automatically detect the patch shape and correct for any shape distortion. It then determines the location of all the UV sensitive squares and reference colors. Specifically, the first step is to determine whether a heart shape is present and its general position in the image, these are achieved by using Haar feature based cascade classifiers, which are trained using a large number of both positive images and negative images.
  • the heart is then isolated from the image.
  • the second step is to detect the shape more closely using feature matching, and further correct distortions using perspective control. Once the key points on the heart shape have been detected, the reference color squares and UV sensitive ink squares are then located using the template.
  • the app takes multiple scans of the patch and every scan passes through a quality control process, which includes elimination of scans with uneven illumination and uneven light reflection.
  • the images are then color corrected and white balance corrected. Only the best quality images are accepted and used for color quantification.
  • the colors are sampled from each reference color square and all UV sensitive ink squares. During the color sampling, the color histogram for each square is calculated and the center 50% of the pixel colors remain for further processing. This step is to remove wrinkles, light reflection and shadows resulting in reduced noise in the image.
  • the sampled colors from each reference color squares are then compared to the "true color", which is pre-determined by the color code of the inks. The color correction is performed for each square and the same correction matrix is applied to its surrounding UV sensitive ink squares.
  • the algorithm takes measurements of the color of the UV sensitive dyes and compares them to the reference colors.
  • the reference colors are closely matched to the color of the UV sensitive dyes and mimic UV dyes at different UV exposure levels. This allows for accurate color quantification at different lighting conditions, since any particular lighting condition affects the reference colors and UV dye colors to similar extent.
  • the UV variable ink square is matched to the closest reference color square by comparing ⁇ .
  • the UVA is interpolated between the UVA values that correspond to the two closest reference colors (E.q.2).
  • the boundary condition and minimal scanning frequency are set as Fig. 3 boundary condition and frequent scan condition. These are to further remove the noise of the readings.
  • UV Dose Determination In order to determine user's personal UV exposure levels and provide accurate
  • the algorithm takes into account many parameters.
  • the corresponding UVB exposure is calculated using a pre- computed lookup table that gives the conversion factor as a function of the column amount of ozone in the atmosphere and solar zenith angle (SZA).
  • SZA is determined based on GPS location and time. The user latitude, longitude, and time is also used to extract the forecast ozone amount from satellite-measurements. In this conversion, the effects of clouds and aerosols are assumed to be similar at UVA and UVB wavelengths.
  • UVA and UVB results are then cross-checked with the maximal values expected for the user location determined based on UVI forecasting webservices. Again, precomputed lookup tables, which are functions of ozone and SZA, are used to relate the quantities. This process prevents sporadic and erroneous readouts. If internet connection is not available, the result is cross-checked with lookup tables that relate maximal UVI data with corresponding maximal values for UVA and UVB at different geographical locations and time.
  • the wavelength threshold between UVB and UVA is 315 nm.
  • the personal daily safe UV doses are calculated based on the skin phototype and minimal erythema dose (MED) (Fig. 1 lA,Table 1).
  • MED minimal erythema dose
  • the skin photo type is determined according to the Fitzpatrick phototyping scale, on a simplified user questionnaire completed by the user when the user first opens the app.
  • the maximal daily safe UV dose is set to 0.4 MED for each skin phototype and it is based on studies demonstrating that some degree of UV induce skin damage can be observed after exposure to 0.5 MED.
  • the rate of change of the UV exposure throughout the day is defined as "exposure” and it is calculated for every scan for the time between the current and previous patch scan. It is divided into 3 zones: 1 ) Green - on track to stay within the daily safe UV dose; 2) Orange - at risk to exceed the daily safe UV dose; 3) Red - high risk of UV overexposure (Fig. 1 I B, Table 2). Sensor Validation:
  • the patch was then evaluated on human volunteers in two clinical studies.
  • the first 5-day study with 14 volunteers was designed to test device functionality in different real life activities including swimming, beach activities, showering, and compatibility with sunscreen and skin care product applications.
  • the second study was designed to test the patch UV readout accuracy during controlled and real life daily activities. The subjects received an average of
  • the UV sensor patch is compatible with sunscreen. Measured by the UV sensor patch, the sunscreen greatly reduced the UV exposure during an intermittent UV exposure in the morning, afternoon and evening.
  • the UVA exposure was 0.071 1 ⁇ 0.0215 MJ/m 2 , 0.1716 ⁇ 0.0581 MJ/m 2 , 0.1 861 ⁇ 0.0600 MJ/m 2 measured at 1 1 :50 am, 2:45 pm and 6: 13 pm, respectively.
  • the UVA exposure was 0.0021 ⁇ 0.0047 MJ/m 2 , 0.0061 ⁇ 0.0084 MJ/m 2 and 0.01 1 1 ⁇ 0.0139 MJ/m 2 , respectively.
  • FIG. 15 shows average personal UV exposure levels based on the My UV Patch app user data .
  • Maximum UV exposure for each cell phone device is collected and averaged within each country (Fig. 15 A, 15C) and state in US (Fig. 15B). Data from a total of 39 countries and 26 US states were received between June 6, 2016 and August 1 8, 2016, and were processed for the map.
  • UVI ultraviolet Index
  • UVI represents the strength of sunburn-producing UV radiation. It is a scaled version of the erythemally weighted irradiance falling on a horizontal surface; therefore, it implicitly includes a zenith angle cosine dependence.
  • the personal exposure can be quite different from the idealized case of the radiation on a horizontal surface.
  • personal UV exposure can either be greater or less than the exposure predicted from UVI, sometimes by factors larger than 30%>.
  • the real UV exposure can be less than 50%) of UVI.
  • the UV dose received by human skin depends also on body-site. For example, UV exposure on the thigh will generally be less than on the top of the head or shoulder.
  • UVI ultraviolet-infrared irradiation
  • UVB ultraviolet-infrared irradiation
  • UVA doses are typically 20 or 30 times greater than UVB doses.
  • UVA does not contribute to suntan or sunburn as much as UVB
  • people are often not aware of excessive UVA exposures, especially on cloudy days or in indoor environments.
  • skin damage from UV exposure is not immediately apparent.
  • the erythemal reaction can occur more than 12 hours after exposure making it difficult for an average person to know what is the safe amount of UV radiation.
  • the objective of this project was to design and develop a low cost, wearable UV sensor for accurate quantification of personal UV exposures and degree of protection by sunscreens.
  • MyUV patch provides continuous personal UV exposure monitoring with or without sunscreen applied and provides the user with recommendations for better UV protection. It is stretchable, breathable, and has similar mechanical properties to human skin. The user can apply sunscreen on the patch the same way as it is applied on the rest of the body. The patch then helps to provide information on how much the sunscreen reduced the user's UV exposure.
  • a main advantage of the patch is that it is capable of measuring UV doses in the presence of sunscreen, therefore providing direct measurement of the user's UV exposure when protected with sunscreen.
  • the patch colorimetric analysis showed good correlation to the Scienterra devices.
  • the ultimate test was through the wide distribution of the device to the public in July 2016, and the analysis of the resulting data, number of patches were distributed in countries around the world at no charge through La Roche Posay. This allowed us to acquire data on daily personal UV doses in different geographical locations and relate them to sunscreen usage and UVI in these locations (FIG. 1 5).
  • the reference colors are printed on TPU films (DingZing Advanced Materials Inc., Taiwan) using roller printing.
  • the UV ink and blockers (Spectra Group Inc., USA) are then printed using screen printing with mesh size ranging from 1 1 0 to 380 um.
  • Below the TPU film is the near field communication antenna (NXP semiconductors).
  • the adhesives used in the patch are medical grade (Flexcon Inc., USA).
  • the UV sensor patches are first calibrated under natural sun light with solar UV radiation.
  • the solar UV radiation is measured by electronic UV dosimeters (Scienterra Inc, New Zealand).
  • the Scienterra dosimeters are pre-calibrated against the instruments at the solar irradiance monitoring station in the UV-B monitoring and research program by National Renewable Energy Laboratory (NREL).
  • NREL National Renewable Energy Laboratory
  • the Scienterra dosimeters are also compared with radiative transfer calculations using tropospheric ultraviolet and visible (TUV) radiation model on several clear days in San Francisco.
  • UV intensity is measured using the OAI 308 Meter and a 365 nm probe (OAI Inc. USA) and is kept constant.
  • the images of the UV sensor patch are captured by a Nikon D5100 digital camera (Nikon Inc, USA). Images are processed using a Matlab routine (Mathworks Inc., USA). The response curves of the UV sensor patch are compared between the solar simulator exposure and natural sun light exposure to achieve consistency.
  • the image processing algorithm is written in Matlab.
  • the image processing algorithm is then implemented using C/C++ with the OpenCV library for both Android and iOS apps.
  • Part of the image processing is written in Objective-C for iOS and Java for Android.
  • the visualizing of the world UV map is achieved by a custom web framework built in house using JavaScript, Node.js, require.js, HMTL and CSS.
  • the patch evaluation study was conducted in St. Louis, Florida. On day 1 , subjects walked along the pre-set route in the morning, at noon and in the afternoon for four miles, respectively. On day 2, subjects conducted beach activity for two hours and followed by one hour free city walk following a pre-determined route. On day 3, subjects repeated day 1 activity with La Roche Posay Anthelios 30 sunscreen applied on the skin as well on one of the UV sensor patches. Subjects scanned the patches with the pre-installed smartphone app. At the same time, patch pictures were also taken by a trained instructor. Patch images, UV dosimeter readings and app readings were compared. The clinical study protocol is approved by the institutional review board (IRB).
  • the smartphone (user terminal) can include circuitry and hardware as is known in the art.
  • the smartphone may include a CPU, a I/O interface, and a network controller such as
  • the CPU may be an APL0778 from Apple Inc., or may be other processor types that would be recognized by one of ordinary skill in the art.
  • the CPU may be implemented on an FPGA, ASIC, PLD or using discrete logic circuits, as one of ordinary skill in the art would recognize.
  • the CPU may be implemented as multiple processors cooperatively working in parallel (such as a cloud computing environment) to perform the instructions of the inventive processes described above.

Abstract

L'invention concerne un système qui permet de déterminer des mesures individuelles de rayonnement ultra-violet (UV) et qui comprend : un dispositif de mesure conçu pour mesurer une exposition aux UV; un dispositif terminal conçu pour recevoir ou capter une sortie mesurée de l'exposition aux UV provenant du dispositif de mesure et pour déterminer un niveau de risque lié à l'exposition individuelle aux UV de l'utilisateur sur la base d'au moins l'exposition solaire mesurée et des informations concernant le type de peau de l'utilisateur particulier. Le dispositif de mesure conçu pour mesurer l'exposition au rayonnement UV comprend une surface qui est pourvue d'une pluralité de sections différentes qui présentent chacune une sensibilité différente à l'exposition au rayonnement UV, et chacune de la pluralité de sections différentes est conçue pour afficher une couleur différente en réponse à l'exposition au rayonnement UV.
PCT/US2017/012108 2016-01-04 2017-01-04 Dispositif et système pour mesures individuelles d'exposition aux uv WO2017120176A1 (fr)

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JP2018534934A JP6735832B2 (ja) 2016-01-04 2017-01-04 個人のｕｖ曝露測定のための装置およびシステム
CN201780005668.7A CN108472499B (zh) 2016-01-04 2017-01-04 用于个人uv暴露测量的设备和系统
KR1020187022529A KR102096018B1 (ko) 2016-01-04 2017-01-04 개인용 uv 노출 측정 장치 및 시스템
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